Cited by 2,680Advanced Clustering Algorithms ResearchData Management and AlgorithmsBayesian Methods and Mixture Models

Density-Based Clustering Based on Hierarchical Density Estimates

Campello, Ricardo J. G. B., Moulavi, Davoud, Sander, Joerg·2013·Lecture Notes in Computer Science

Integrity signals

Registry

Crossref · 10.1007/978-3-642-37456-2_14

Cites

26 of 26 references verified0 citation errors · 0 not checkable

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  • HDBSCAN is adept at clustering outputs from LLMs due to its ability to manage clusters with varied densities, autonomously identify the number of clusters, and filter out noise as outlier data.

    “HDBSCAN is adept at clustering outputs from LLMs due to its ability to manage clusters with varied densities, autonomously identify the number of clusters, and filter out noise as outlier data.”

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Authors
Campello, R. J. G. B., Moulavi, D., & Sander, J.
Affiliations
University of Alberta (CA)
Venue
Lecture Notes in Computer Science
Published
2013
Volume
7819
Pages
160-172
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W151377110
Cited by
2,680
References
27
Citations by year
2026: 413 · 2025: 497 · 2024: 404 · 2023: 337 · 2022: 271 · 2021: 253 · 2020: 181 · 2019: 148 · 2018: 84 · 2017: 50 · 2016: 24 · 2015: 9 · 2014: 9
FWCI
9.26
Type
conference paper
Language
en
Open access
closed
Locations
  • Lecture notes in computer science (publishedVersion)
  • ResearchOnline - JCU (James Cook University) (submittedVersion)
  • Lecture Notes in Artificial Intelligence
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
  • Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
Keywords
density-based clustering, hierarchical clustering, density limit

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